Transformer neural network based real-time process monitoring and direct visualization of top-down vat photopolymerization

Top-down vat photopolymerization (TVPP) technology is rapidly developing to all of the industries for new products development and manufacturing due to its low cost, fast speed and high precision. The powerful capability of TVPP for large size, highly customized and medium batch production renders it one of the most popular additive manufacturing techniques today. An effective real-time process monitoring method providing timely feedback for part defects, especially in case of print failure, is highly desirable but still rarely reported for TVPP 3D printing. Large 3D objects are normally segmented into smaller parts to reduce the risk of failure and materials waste, resulting in the complexity of building while sacrificing component integrity. Herein, a transformer neural network based real-time and visualized process monitoring (TransRV) was constructed as an effective method to enhance the manufacturing performance and quality. Upon the challenge of visualizing and capturing the real-time fabricated layer from the around liquid photoresin, a real-time dataset including in-situ standard reference images and real-time mask fabricated layers was initially constructed. Based on the dataset foundation, we then developed a novel neural network model for effective segmentation of captured images by introducing multiple attention mechanisms and adopting the architecture of Swin Transformer. The experimental results showed that the real-time taken images during the printing process could be accurately segmented through our designed neural network model. The mIoU, which is the ratio of mean intersection over union, was considered as the main evaluation index in the test set. And the value of mIoU could achieve as high as 96.14 %. On the basis of this result, we further constructed a multiple quality monitoring indicator for quality assessment and defect detection of TVPP process. It was proved that this indicator enabled real-time accurate recognition and in time feedback. The typical defects such as overall collapse and partial missing of the printed parts that usually occur during TVPP process would be timely detected and subsequently stopped printing. Apparently, the methods developed in this work provide a promising strategy to effectively eliminate the material waste and highly improve the productivity. Most importantly, the presented real-time process monitor holds the great potential for quality control and defect detection of widespread TVPP manufacturing.